Search Results for author: Ildikó Pilán

Found 9 papers, 4 papers with code

Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis

no code implementations22 Oct 2023 Anthi Papadopoulou, Pierre Lison, Mark Anderson, Lilja Øvrelid, Ildikó Pilán

The text sanitization process starts with a privacy-oriented entity recognizer that seeks to determine the text spans expressing identifiable personal information.

Language Modelling named-entity-recognition +2

Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis

1 code implementation27 Sep 2023 Ildikó Pilán, Laurent Prévot, Hendrik Buschmeier, Pierre Lison

Scripted dialogues such as movie and TV subtitles constitute a widespread source of training data for conversational NLP models.

Language Modelling Large Language Model

Bootstrapping Text Anonymization Models with Distant Supervision

1 code implementation LREC 2022 Anthi Papadopoulou, Pierre Lison, Lilja Øvrelid, Ildikó Pilán

Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals.

Language Modelling Text Anonymization

Building a Norwegian Lexical Resource for Medical Entity Recognition

no code implementations6 Apr 2020 Ildikó Pilán, Pål H. Brekke, Lilja Øvrelid

We present a large Norwegian lexical resource of categorized medical terms.

Candidate sentence selection for language learning exercises: from a comprehensive framework to an empirical evaluation

no code implementations12 Jun 2017 Ildikó Pilán, Elena Volodina, Lars Borin

We present a framework and its implementation relying on Natural Language Processing methods, which aims at the identification of exercise item candidates from corpora.

Sentence

Detecting Context Dependence in Exercise Item Candidates Selected from Corpora

no code implementations6 May 2016 Ildikó Pilán

We explore the factors influencing the dependence of single sentences on their larger textual context in order to automatically identify candidate sentences for language learning exercises from corpora which are presentable in isolation.

A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity

no code implementations29 Mar 2016 Ildikó Pilán, Sowmya Vajjala, Elena Volodina

Corpora and web texts can become a rich language learning resource if we have a means of assessing whether they are linguistically appropriate for learners at a given proficiency level.

Sentence

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